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arXiv · 2505.16505

Sparse Activation Editing for Reliable Instruction Following in Narratives

Runcong Zhao·Chengyu Cao·Qinglin Zhu·Xiucheng Lv·Shun Shao·Lin Gui·Ruifeng Xu·Yulan He
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Abstract

Complex narrative contexts often challenge language models' ability to follow instructions, and existing benchmarks fail to capture these difficulties. To address this, we propose Concise-SAE, a training-free framework that improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. To thoroughly evaluate our method, we introduce FreeInstruct, a diverse and realistic benchmark of 1,212 examples that highlights the challenges of instruction following in narrative-rich settings. While initially motivated by complex narratives, Concise-SAE demonstrates state-of-the-art instruction adherence across varied tasks without compromising generation quality.

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Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Lv, Shun Shao, Lin Gui, Ruifeng Xu, Yulan He. 2025-05-22. Sparse Activation Editing for Reliable Instruction Following in Narratives. https://arxiv.org/abs/2505.16505

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